Digital technology adoption and knowledge flows within firms: Can the Internet overcome geographic and technological distance?

Under what conditions does digital technology adoption increase cross location knowledge flows within firms? We investigate this question by studying the impact of adopting basic Internet access on cross-location knowledge flows within the same firm. We construct a large data set of Internet adoption and patent citations among dyadic pairs of firm-locations between 1992–1998. We find that when both locations in the pair adopt basic Internet there is an increase in the likelihood of a citation between the citing and (potential) cited location. In contrast, we find no significant effect of Internet adoption at only the citing location. We further study how this effect varies according to the proximity of the research activities between the source and recipient of knowledge and specialization of the research activities within the recipient. We find that the likelihood of a citation increases more after dyadic Internet adoption when the pair is working in similar research areas and when the research areas in the citing location are less specialized. These results, which are robust to a range of robustness analyses, suggest that digital technologies such as Internet connectivity are able to facilitate knowledge flows between locations only when they share a common knowledge base.     

Does it matter where patent citations come from? Inventor vs. examiner citations in European patents

This paper addresses the question of whether patent citations are useful indicators of technology flows. We exploit the distinction between citations added by inventors and patent examiners. We use information from the search reports of European Patent Office patent examiners to construct our dataset of patenting activity in Europe and the US, and apply various econometric models to investigate what determines the probability that a citation is added by the inventor rather than the examiner. Contrary to previous work which uses US Patent and Trademark Office data, we find that geographical distance is a factor that strongly diminishes the probability of knowledge flows. We find other significant effects of such factors as cognitive distance, time and strategic factors on citing behaviour.     

Quantifying the structural and temporal characteristics of negative links in signed citation networks

Although the citation relationships among papers can help in tracking and understanding the development of knowledge, few studies have noted that the content and sentiments of citations of a paper differ. Here, we use sentiment-labeled citation data to construct a directed signed citation network, in which an author may agree with or criticize the cited paper and these represent different ways of inheriting knowledge. The dataset we use consists of 9,038 papers in the field of Computational Linguistics, including 25,275 citations, with 20.8% positive citations, 8.6% negative citations and 70.6% neutral citations. We systematically quantify the structural patterns of negative citations, impact assortativity of involved papers, occurrence time distribution and consequences of receiving negative attention. Remarkably, we find that papers with different impacts have a similar probability of receiving negative citations, and highly cited papers tend to give negative citations to low-impact papers around but avoid giving negative citations to high-impact papers. Our research also reveals the random occurrence rules and colocation patterns of negative citation distribution. In addition, we show that, in the short term, around 60% of multiple negative citations is positively related to the impact of the cited paper while more than 80% are negatively related to the impact in the long run. Our findings explain the pattern by which negative citations occur and deepen the understanding of negative citations.     

Startup size and the mechanisms of external learning: increasing opportunity and decreasing ability?

An important area of investigation in the field of entrepreneurship examines how people and organizations exploit technological opportunities. Prior research suggests that alliances, the mobility of experts, and the informal mechanisms associated with geographic co-location can present firms with useful opportunities to source technological knowledge. This paper uses insights from strategic management and organizational theory to suggest that organizational size may have an important impact on the extent of external learning, since it differentially affects the likelihood of learning via formal and informal mechanisms.Examining a cross-section of semiconductor startups, we find that external learning increases with startup size. With regard to the specific mechanisms of learning, we find that firms learn from alliances regardless of their size. For the informal mechanisms of mobility and geographic co-location, however, learning decreases with firm size. These results suggest that as startups grow, they may have increasing opportunities to access and exploit external knowledge, but their motivation (and hence ability) to learn from more informal sources may decrease.     

Using patent citation patterns to infer innovation market competition

We propose an empirical strategy to estimate competition in innovation markets. Our method relates firms’ market return on equity to information about patent citation patterns. Two innovations are implemented in the methodology. First is the application of daily abnormal stock returns rather than annual measures of Tobin's q. Second is the creation of citation patterns related to the area of science a firm patents in as represented by the detailed patent classification system. We find that markets positively reward firms when patents are granted. We further find that firm's market value increases when its patent portfolio is cited. We find evidence of competition in innovation markets. The market reacts at the time that the citation occurs and does not anticipate future citations at the time of patenting. Holding this effect constant, we find that citations from patents in the same area of science tend to reduce market value. We interpret these findings as consistent with more citations indicating more valuable intellectual property but citations from competing technologies decreasing it.     

Methods to account for citation inflation in research evaluation

Quantitative research evaluation requires measures that are transparent, relatively simple, and free of disciplinary and temporal bias. We document and provide a solution to a hitherto unaddressed temporal bias – citation inflation – which arises from the basic fact that scientific publication is steadily growing at roughly 4% per year. Moreover, because the total production of citations grows by a factor of 2 every 12 years, this means that the real value of a citation depends on when it was produced. Consequently, failing to convert nominal citation values into real citation values produces significant mis-measurement of scientific impact. To address this problem, we develop a citation deflator method, outline the steps to generalize and implement it using the Web of Science portal, and analyze a large set of researchers from biology and physics to demonstrate how two common evaluation metrics – total citations and h-index – can differ by a remarkable amount depending on whether the underlying citation counts are deflated or not. In particular, our results show that the scientific impact of prior generations is likely to be significantly underestimated when citations are not deflated, often by 100% or more of the nominal value. Thus, our study points to the need for a systemic overhaul of the counting methods used evaluating citation impact – especially in the case of researchers, journals, and institutions – which can span several decades and thus several doubling periods.     

跨学科输入知识对学科发展的影响力分析——以图书情报学科为例

【目的/意义】跨学科输入知识是指其他学科（跨学科）的知识被目标学科多次引用,并逐渐成为目标学科的研究内容,刺激知识创新与生长,进而促进学科发展。因此,识别跨学科输入知识,测量其对目标学科发展的影响力,有利于分析学科进步的跨学科来源及作用力大小,有利于把握学科创新发展的现状与趋势。【方法/过程】基于跨学科引用,从跨学科输入知识在目标学科的研究热度、研究广度、研究深度3个方面,构建跨学科输入知识对目标学科发展的影响力模型,测度影响力程度。并以图书情报学跨学科程度较高的6种期刊数据为研究对象进行实证研究。【结果/结论】通过影响力模型,测量跨学科输入知识对图书情报学的影响力大小,并根据趋势分析法将跨学科知识的影响力趋势分为上升型、稳定型和下降型。【创新/局限】本文构建跨学科知识对学科发展的影响力模型并验证其有效性,后续将进一步分析跨学科知识对目标学科的作用点,识别跨学科知识生长点。     

基于AVMS模型的学科交叉可视化研究——以图书情报学为例

以中国期刊全文数据库(CNKI)和万方为来源数据库,统计分析了国内图书情报学(Library and Information Science,简称LIS)有关学科交叉研究的文献。基于AVMS(Author-Viewpoint-Method-Subject)模型对检索出图书情报学有关学科交叉研究的60篇文献进行可视化分析,结果表明:图书情报学学科交叉多以引文为研究视角,不乏文献计量(文章、作者和引文)、跨学科发文等创新视角;多以引文分析、社会网络分析、共词分析为研究方法,不乏聚类分析、调查法、SWOT分析法等方法;主题研究集中与图书情报学具体交叉学科研究、学科交叉程度测度和交叉学科的主题研究。     

How status of research papers affects the way they are read and cited

Although citations are widely used to measure the influence of scientific works, research shows that many citations serve rhetorical functions and reflect little-to-no influence on the citing authors. If highly cited papers disproportionately attract rhetorical citations then their citation counts may reflect rhetorical usefulness more than influence. Alternatively, researchers may perceive highly cited papers to be of higher quality and invest more effort into reading them, leading to disproportionately substantive citations. We test these arguments using data on 17,154 randomly sampled citations collected via surveys from 9,380 corresponding authors in 15 fields. We find that most citations (54%) had little-to-no influence on the citing authors. However, citations to the most highly cited papers were 2–3 times more likely to denote substantial influence. Experimental and correlational data show a key mechanism: displaying low citation counts lowers perceptions of a paper's quality, and papers with poor perceived quality are read more superficially. The results suggest that higher citation counts lead to more meaningful engagement from readers and, consequently, the most highly cited papers influence the research frontier much more than their raw citation counts imply.     

Knowledge flows - Analyzing the core literature of innovation, entrepreneurship and science and technology studies

This paper applies network analysis to a citation database that combines the key references in the fields of Entrepreneurship (ENT), Innovation Studies (INN) and Science and Technology Studies (STS). We find that citations between the three fields are relatively scarce, as compared to citations within the fields. As a result of this tendency, a cluster analysis of the publications in the database yields a partition that is largely the same as the a priori division into the three fields. We take this as evidence that the three fields, although they share research topics and themes, have developed largely on their own and in relative isolation from one another. We also apply a so-called ‘main path’ analysis aimed at outlining the main research trajectories in the field. Here we find important differences between the fields. In STS, we find a cumulative trajectory that develops in a more or less linear fashion over time. In INN, we find a major shift of attention in the main trajectory, from macroeconomic issues to business-oriented research. ENT develops relatively late, and shows a trajectory that is still in its infancy.     

Mapping the importance of the real world: The validity of connectivity analysis of patent citations networks

Recent empirical findings have questioned the use of patent citations as a measure. This points to the need of validation of patent citations methodologies, which we address by testing a recent methodology for studying technological evolution, namely connectivity analysis of citation networks. We find connectivity analysis to be a valid tool to identify the reliable knowledge which opens the way to further technological evolution of a surgical prosthesis, the artificial spinal disc. We also illustrate how connectivity analysis represents how this reliable knowledge differs depending on the stage of technological evolution. The corroborated validity of connectivity analysis of patent citations may trigger a renaissance in the use of this kind of patent data.     

基于引文内容分析的交叉领域知识内化过程研究

【目的/意义】不同学科知识的相互整合是交叉研究领域形成和发展的基础。对交叉领域知识整合过程的理解有助于促进交叉领域的知识发现和知识创新。【方法/过程】本文利用自然语言处理技术,从交叉领域文献题录数据、引文上下文和参考文献文本信息中抽取知识短语,以引证关系和词汇匹配关系为线索,提出吸纳知识和内化知识的识别方法,深入揭示交叉领域中“知识吸纳-知识内化”的知识整合过程。【结果/结论】以电子健康领域（eHealth）为例,揭示了该领域的知识内化总体特征和不同来源学科所提供知识的内化特征差异。【创新/局限】本文结合引文内容对交叉领域知识内化过程进行量化分析,提出了一种新颖的全文本量化分析方法,为理解交叉领域的知识生长过程提供了新的视角和分析方法。未来研究可以结合参考文献全文以进一步优化这一方法,同时在更多的交叉研究领域开展实证。     

融合主题指纹-引文的方法逻辑：理论阐释与数据呈现

【 目的/意义】从文献实体的多特征数据融合的视角构建基于主题指纹-引文耦合的数据融合理论模型,并 对数据融合过程进行实证分析。【方法/过程】对融合主题指纹-引文的方法逻辑进行理论阐释和数学机理分析,以 乳腺小叶癌为案例呈现文献特征项的数据融合过程,通过主题指纹的类别归属与引文期刊所属JCR学科的对比发 现二者在揭示知识表征方面的功能特征。【结果/结论】融合主题指纹-引文的2-模知识网络能够发挥主题指纹和引 文共同揭示学科主题和知识结构的功能;引文信息表征了研究的学科基础、学科背景等稳定性知识结构信息,主题 指纹代表了学科研究前沿、突变主题、新兴趋势等动态性知识主题信息。【创新/局限】融合主题指纹-引文的理论模 型和分析方法是从数据融合层面将内容分析方法与引文分析方法进行结合的有效尝试,未来的研究将聚焦多模知 识网络构建、网络结构分析和量化测度研究,进一步提高该理论模型在知识服务领域应用的科学性、普适性。     

标准引用与论文引用的比较研究

标准和论文是科技成果的重要文献载体,具有重要的情报价值。本文通过文献调研法,从引用范围、引用目的、引用动机、关联性等维度比较了标准引用与论文引用的异同点。结论是标准引用与论文引用在表现出相似的知识关联关系和关联性,两者在引用条件、引用范围、引用目的、出版物性质、引用主体、引用内容选择等方面存在差异。     

基于趋势分析的学科研究主题优先级战略坐标

对学科领域研究主题优先级进行战略分析,能够帮助科研人员及科研管理决策部门快速了解学科领域的研究态势、发现科学前沿,对提高科研产出起到积极的支持和促进作用。本文以图书情报学研究主题为例,采用主题提取与趋势分析相结合的方法,在提取学科主题基础上,从发文趋势和引文趋势两个维度,绘制含“研究贫乏区、热点区、冷点区、过热区”的我国图书情报学领域研究主题战略坐标。研究表明：本文提出的趋势战略坐标能够有效展示学科领域不同研究主题的发展阶段,全面、细致地呈现不同研究主题的发展等级。     

基于文本相似度分析的期刊引文有效性识别研究

真实性、准确性、直接性与完整性是引用参考文献的首要准则,然而种种研究表明,当前的研究论著中引而不注、过度引用、模糊标注甚至虚假引用等不当引用行为日渐增多,这极大地影响了科学研究领域的学术风气,给读者阅读、期刊审稿及成果评定等工作带来了许多不便。本文利用文本相似度算法建立期刊引文有效性识别方法,试图从海量期刊引文数据中识别出期刊引文的真实有效性。实验表明,本文的期刊引文有效性识别方法在引文有效性方面实现了较好的识别效果,可为虚假引文的识别工作提供可靠的依据,从而为编辑人员发现、修正虚假引用问题提供帮助,彰显科学研究的严谨务实精神。     

Applicant and examiner citations in U.S. patents: An overview and analysis

Prior art patent citations have become a popular measure of patent quality and knowledge flow between firms. Interpreting these measurements is complicated, in some cases, because prior art citations are added by patent examiners as well as by patent applicants. The U.S. Patent and Trademark Office (USPTO) adopted new reporting procedures in 2001, making it possible to measure examiner and applicant citations separately for the first time. We analyzed prior art citations listed in all U.S. patents granted in 2001-2003, and found that examiners played a significant role in identifying prior art, adding 63% of citations on the average patent, and all citations on 40% of patents granted. An analysis of variance found that firm-specific variables explain most of the variation in examiner-citation shares. Using multivariate regression, we found that foreign applicants to the USPTO had the highest proportion of citations added by examiners. High-volume patent applicants had a greater proportion of examiner citations, and a substantial number of firms won patents without listing a single applicant citation. In terms of technology, we found higher examiner shares among patents in electronics, communications, and computer-related fields. Taken together, our findings suggest that firm-level patenting practices, particularly among high-volume applicants, have a strong influence on citation data and merit additional research.     

Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.     

中国国际科技合著论文的学科分布差异

 本文研究了十年间（1996-2005）SCIE论文中国国际科技合著论文的学科分布差异。通过对论文数量和引文影响的相对水平进行比较,发现在基础研究、工程技术和生物医学三个主要学科大类之间存在明显的差异：基础研究学科论文和引文数量都比较多;工程技术学科论文数多而引文数量相对较少;医学生物学则是论文数量较少而引文数量相对较多。同时,本文还将h指数的概念引入到学科比较中,以学科作为论文产出的主体进行比较分析。同样可以发现三大类学科的差异：基础研究学科h指数普遍较高,工程技术类普遍较低,而医学生物学处于二者之间的地位。     

跨学科团队的知识创新及其演化特征——基于创新单元和创新个体的双重视角

特定的社会化科学结构与主体创造性思维的交互作用是跨学科研究的创意来源。基于社会网络理论和创造性思维理论分析跨学科团队的知识创新过程,引入创新单元和创新个体之间的知识创新与认知扩展的互动关系视角建立跨学科团队的知识创新模型,揭示多学科知识和创造性思维在内的异质性整合和认知调整,解释创新个体间知识关联与运动促进认知发展过程,和实现跨学科团队知识创新目标的本质。通过对模型性能及影响因素的仿真分析得出,累进式知识创新和激进式知识创新改变网络的知识分布状况,共同扩充个人和群体的知识存量,提升跨学科团队整体知识水平。